Implementation of Ensemble Method in Schizophrenia Identification Based on Microarray Data

Purba, Diya Namira; Nhita, Fhira; Kurniawan, Isman

doi:10.29207/resti.v6i1.3788

Cited by 6 publications

(3 citation statements)

References 16 publications

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Section: Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

“…XGBoost is one of the learning techniques that optimizes faster because it has been optimized with increasing gradients [8][28] [29]. XGBoost has been widely used because of its fast, efficient, and scalable performance [29]. The principle of XGBoost is to achieve accurate prediction results through the iterative calculation of decision tree classification.…”

Section: Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

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Performance Analysis of Hybrid Machine Learning Methods on Imbalanced Data (Rainfall Classification)

Gumilar

Prasetiyowati

Sibaroni

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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This study proposes several methods to analyze the performance of the hybrid machine learning method using Voting and Stacking on rainfall classification. The two hybrid methods will combine five classification methods, namely Logistic Regression, Support Vector Machine, Random Forest, Artificial Neural Network, and eXtreme Gradient Boosting. The data used is Bandung City rainfall data for the years 2005 until 2021. The hybrid method is classified as an ensemble, which means combining several individual classification models to improve the performance of the built model. Voting algorithm has weaknesses in imbalanced data, while stacking does not. The results show that by combining five machine learning methods on an imbalanced dataset, the Stacking algorithm obtains an accuracy value of 99.60%. Meanwhile, with the addition of the SMOTE technique, the accuracy increases to 99.71%. This is supported by the performance of the Stacking method which is superior because it takes the best classification value for each individual model and can overcome the imbalance. Model evaluation does not only focus on accuracy, but also precision, recall, and f1-score. The contribution of this research is to provide information about the best Hybrid method between Voting and Stacking in obtaining model performance results on rainfall classification.

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Section: Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

Section: Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

Performance Analysis of Hybrid Machine Learning Methods on Imbalanced Data (Rainfall Classification)

Gumilar

Prasetiyowati

Sibaroni

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…The formula for each validation parameter is presented in Eq. ( 9) -( 12) (Manju et al, 2019;Purba et al, 2022)…”

Section: Model Validationmentioning

confidence: 99%

Classification of Non-Small Cell Lung Cancer Based on Gene Expression in Cases of Smokers and Non-Smokers using Ensemble Methods with Statistical Based Feature Selection

Nhita¹,

Kurniawan²

2022

Journal of Computer Science

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Lung cancer is one of the leading causes of death globally. One of the main risk factors for lung cancer is smoking, which causes more than 90% of lung cancer cases. There are two types of lung cancer, i.e., Small Cell Lung Cancer (SCLC) and Non-Small Cell Lung Cancer (NSCLC), which the latter is the most common. One method that can be used to detect cancer is the implementation of machine learning on gene expression data. Machine learning is one approach that promises good performance in classifying gene expression data. This study aimed to predict the existence of NSCLC based on gene expression, whether including NSCLC or normal. We used three data sets, i.e., GSE10072, GSE19804, and GSE19188, which relate to the cases of NSCLC in smokers and nonsmokers. The prediction was carried out using six Ensemble Methods, i.e., Random Forest, Adaptive Boosting, Extra Tree, Gradient Boosting, Extreme Gradient Boosting, and Categorical Boosting. Feature selection was carried out by calculating the correlation between feature and target according to statistical parameters, i.e., ANOVA, Mutual Information (MI), and a combination of ANOVA and MI. We obtained the prediction model that outperformed the related studies for two similar data sets with the value of accuracy for the GSE10072, GSE19804, and GSE19188 data sets 100%, 97.22%, and 100%, respectively.

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Implementation of Genetic Algorithm-Support Vector Machine on Gene Expression Data in Identification of Non-Small Cell Lung Cancer in Nonsmoking Female

Rohimat

Nhita

Kurniawan

2022

2022 5th International Conference of Computer and Informatics Engineering (IC2IE)

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Implementation of Ensemble Method in Schizophrenia Identification Based on Microarray Data

Cited by 6 publications

References 16 publications

Performance Analysis of Hybrid Machine Learning Methods on Imbalanced Data (Rainfall Classification)

Performance Analysis of Hybrid Machine Learning Methods on Imbalanced Data (Rainfall Classification)

Classification of Non-Small Cell Lung Cancer Based on Gene Expression in Cases of Smokers and Non-Smokers using Ensemble Methods with Statistical Based Feature Selection

Implementation of Genetic Algorithm-Support Vector Machine on Gene Expression Data in Identification of Non-Small Cell Lung Cancer in Nonsmoking Female

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